Federated Learning for Secure Blockchain-Based Identity Verification in Decentralized Systems
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Abstract
Due to a growing number of decentralized solutions announced during the last years there is a need for private and efficient identity verification. Various issues related to the traditional identity management systems include security, issues that make them have single points of failure, and high operational costs. It is for this reason that Federated Learning (FL) tends to be viewed as a practical and effective solution, as it allows the training of models in a decentralized environment by participating nodes while keeping the data confidential. The integration of FL with blockchain technology is proposed in the research work presented in this paper to create the FL-BIV framework that offer better security, scalability, and privacy in identity management. The proposed system also applies FL to train the identity verification models for multiple participants with data privacy. Blockchain helps individuals to maintain various accreditation attributes and verification records to protect the identity and to have confidence in decentralized systems. Our approach works towards some of the major issues such as the data poisoning attack, adversarial attack, and Byzantine failure by using secure aggregation and differential privacy. The results of the experiment reveal that our approach is promising and that it enhances the accuracy of authentication as well as minimizes costs in contrast to the centralized and decentralized approaches. The presented FL-BIV framework improves the security level, eliminates the necessity for the authorization of third parties, and increases user control. This work helps to build philosophies for the highly effective decentralized identity verification in various decentralized networks, which would help create secure, scalable, and efficient authenticating mechanism in decentralized facility which includes Decentralized Finance (DeFi), Internet of Things (IoT), metaverse among others.
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